From 0f7d8427c3abc46eb3215485776d9eb4f312be90 Mon Sep 17 00:00:00 2001 From: Steven Braun Date: Fri, 3 Nov 2023 08:23:38 +0100 Subject: [PATCH] Move multivariate normal example script into notebook --- notebooks/multivariate_normal.ipynb | 384 ++++++++++++++++++ .../distributions/multivariate_normal.py | 135 ------ 2 files changed, 384 insertions(+), 135 deletions(-) create mode 100644 notebooks/multivariate_normal.ipynb diff --git a/notebooks/multivariate_normal.ipynb b/notebooks/multivariate_normal.ipynb new file mode 100644 index 0000000..6e6a188 --- /dev/null +++ b/notebooks/multivariate_normal.ipynb @@ -0,0 +1,384 @@ +{ + "cells": [ + { + "cell_type": "raw", + "source": [], + "metadata": { + "collapsed": false + }, + "id": "9a28b9f28498aa6b" + }, + { + "cell_type": "markdown", + "source": [ + "# Fitting an Einsum Network (Einet) on Synthetic 2D Data\n", + "\n", + "In this tutorial, we'll go through the steps of fitting an Einsum Network (Einet) with a multivariate normal distribution on synthetic 2D data. We'll start by generating synthetic data, then initialize and train our Einet model, and finally visualize the results." + ], + "metadata": { + "collapsed": false + }, + "id": "9e82947744dd1b8f" + }, + { + "cell_type": "code", + "execution_count": 58, + "outputs": [], + "source": [ + "import torch\n", + "import torch.optim as optim\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "torch.manual_seed(1)\n", + "np.random.seed(1)\n", + "\n", + "sns.set_style(\"whitegrid\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-03T07:06:19.619117Z", + "start_time": "2023-11-03T07:06:19.591363Z" + } + }, + "id": "a1c2d8e300197847" + }, + { + "cell_type": "markdown", + "source": [ + "## Generating Synthetic Data\n", + "\n", + "Next, we define a function to generate our synthetic 2D data. This data will be created from multiple anisotropic Gaussian distributions to provide a mix of points." + ], + "metadata": { + "collapsed": false + }, + "id": "b04fb277b993057c" + }, + { + "cell_type": "code", + "execution_count": 59, + "outputs": [], + "source": [ + "def generate_data(num_samples=100):\n", + " # Parameters for first Gaussian blob\n", + " mean1 = [2.0, 3.0]\n", + " cov1 = [[1.0, 0.9], [0.9, 0.5]]\n", + "\n", + " # Parameters for second Gaussian blob\n", + " mean2 = [-1.0, -2.0]\n", + " cov2 = [[0.4, -0.1], [-0.1, 0.3]]\n", + "\n", + " # Parameters for third Gaussian blob\n", + " mean3 = [4.0, -1.0]\n", + " cov3 = [[0.3, 0.2], [0.2, 0.5]]\n", + "\n", + " # Parameters for fourth Gaussian blob\n", + " mean4 = [-3.0, 2.0]\n", + " cov4 = [[0.5, -0.2], [-0.2, 0.3]]\n", + "\n", + " # Generate data points\n", + " data1 = np.random.multivariate_normal(mean1, cov1, num_samples // 4)\n", + " data2 = np.random.multivariate_normal(mean2, cov2, num_samples // 4)\n", + " data3 = np.random.multivariate_normal(mean3, cov3, num_samples // 4)\n", + " data4 = np.random.multivariate_normal(mean4, cov4, num_samples // 4)\n", + " data = np.vstack([data1, data2, data3, data4])\n", + "\n", + " return torch.tensor(data, dtype=torch.float32)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-03T07:06:19.619340Z", + "start_time": "2023-11-03T07:06:19.597096Z" + } + }, + "id": "8bfa72b8ed6e7fd1" + }, + { + "cell_type": "markdown", + "source": [ + "## Data Visualization\n", + "\n", + "Visualizing our synthetic data helps us understand its structure and distribution." + ], + "metadata": { + "collapsed": false + }, + "id": "6417262cf8506dce" + }, + { + "cell_type": "code", + "execution_count": 60, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/t3/57fhyt955l9d7dby5dmlzs900000gn/T/ipykernel_76797/1585833564.py:19: RuntimeWarning: covariance is not symmetric positive-semidefinite.\n", + " data1 = np.random.multivariate_normal(mean1, cov1, num_samples // 4)\n" + ] + }, + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_samples = 400\n", + "data = generate_data(n_samples)\n", + "plt.scatter(data[:, 0], data[:, 1], alpha=0.6)\n", + "plt.title(\"Synthetic Data\")\n", + "plt.xlabel(\"$X_1$\")\n", + "plt.ylabel(\"$X_2$\")\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-03T07:06:19.691197Z", + "start_time": "2023-11-03T07:06:19.600051Z" + } + }, + "id": "1b9834ec545259b8" + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + }, + "id": "709de92f68440fd" + }, + { + "cell_type": "markdown", + "source": [ + "## Einet Model Initialization\n", + "\n", + "We then proceed to configure and initialize our `Einet` model with the `EinetConfig` class." + ], + "metadata": { + "collapsed": false + }, + "id": "2035fc494878579f" + }, + { + "cell_type": "code", + "execution_count": 61, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Einet(\n", + " EinetConfig(num_features=2, num_channels=1, num_sums=10, num_leaves=4, num_repetitions=1, num_classes=1, depth=0, dropout=0.0, leaf_type=, leaf_kwargs={'cardinality': 2}, layer_type='linsum')\n", + " (leaf): FactorizedLeaf(\n", + " num_features=1, num_features_out=1\n", + " (base_leaf): MultivariateNormal(num_features=2, num_leaves=4, out_shape=(N, 2, 4))\n", + " )\n", + " (layers): ModuleList(\n", + " (0): SumLayer(num_features=1, num_sums_in=4, num_sums_out=1, num_repetitions=1, dropout=0.0, out_shape=(N, 1, 1, 1))\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "from simple_einet.einet import Einet, EinetConfig\n", + "from simple_einet.layers.distributions.multivariate_normal import MultivariateNormal\n", + "\n", + "# The model will be trained to fit the synthetic data\n", + "num_features = 2\n", + "num_channels = 1\n", + "num_leaves = 4\n", + "num_repetitions = 1\n", + "cardinality = 2\n", + "\n", + "cfg = EinetConfig(\n", + " num_features=num_features,\n", + " num_channels=num_channels,\n", + " num_leaves=num_leaves,\n", + " depth=0,\n", + " num_repetitions=num_repetitions,\n", + " num_classes=1,\n", + " leaf_type=MultivariateNormal,\n", + " leaf_kwargs={\"cardinality\": cardinality},\n", + ")\n", + "model = Einet(cfg)\n", + "print(model)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-03T07:06:19.699337Z", + "start_time": "2023-11-03T07:06:19.692544Z" + } + }, + "id": "96a9d801483f8e51" + }, + { + "cell_type": "markdown", + "source": [ + "## Training the Model\n", + "\n", + "Using the Adam optimizer, we train our model on the synthetic data. We use the negative log-likelihood as our loss function, which is typical for probability density estimation tasks." + ], + "metadata": { + "collapsed": false + }, + "id": "6cbefc94bcd960ca" + }, + { + "cell_type": "code", + "execution_count": 62, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [100/1000], Loss: 4.4668803215026855\n", + "Epoch [200/1000], Loss: 4.051097869873047\n", + "Epoch [300/1000], Loss: 3.8294992446899414\n", + "Epoch [400/1000], Loss: 3.687734365463257\n", + "Epoch [500/1000], Loss: 3.3330492973327637\n", + "Epoch [600/1000], Loss: 3.237292766571045\n", + "Epoch [700/1000], Loss: 3.223085880279541\n", + "Epoch [800/1000], Loss: 3.2182984352111816\n", + "Epoch [900/1000], Loss: 3.216794729232788\n", + "Epoch [1000/1000], Loss: 3.2164559364318848\n" + ] + } + ], + "source": [ + "# Setup optimization\n", + "optimizer = optim.Adam(model.parameters(), lr=0.01)\n", + "epochs = 1000\n", + "\n", + "# Training loop to fit the Multivariate Normal model\n", + "for epoch in range(epochs):\n", + " optimizer.zero_grad()\n", + " log_prob = model(data)\n", + "\n", + " # Negative log-likelihood as loss function\n", + " loss = -torch.mean(log_prob)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # Logging to monitor progress\n", + " if epoch % 100 == 99:\n", + " print(f\"Epoch [{epoch+1}/{epochs}], Loss: {loss.item()}\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-03T07:06:20.749468Z", + "start_time": "2023-11-03T07:06:19.698125Z" + } + }, + "id": "66833b9b99324e1e" + }, + { + "cell_type": "markdown", + "source": [ + "## Results Visualization\n", + "\n", + "After training, we sample from the model and visualize these samples alongside our original data. We use seaborn to create density plots that show the distribution learned by the model." + ], + "metadata": { + "collapsed": false + }, + "id": "b265be8488bf3395" + }, + { + "cell_type": "code", + "execution_count": 63, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_data_and_distribution_seaborn(data, samples, model):\n", + " fig, axes = plt.subplots(1, 2, figsize=(12, 6), sharex=True, sharey=True)\n", + "\n", + " # Generate a grid over which we evaluate the model's density function\n", + " x, y = np.linspace(data[:, 0].min(), data[:, 0].max(), 100), np.linspace(\n", + " data[:, 1].min(), data[:, 1].max(), 100\n", + " )\n", + " X, Y = np.meshgrid(x, y)\n", + " grid = np.vstack([X.ravel(), Y.ravel()]).T\n", + "\n", + " # Evaluate the learned density function over the grid\n", + " with torch.no_grad():\n", + " grid_tensor = torch.tensor(grid, dtype=torch.float32)\n", + " log_prob = model(grid_tensor)\n", + " prob_density = log_prob.exp().numpy().ravel() # Ensure this is 1-dimensional\n", + "\n", + " # Plot for original data points using Seaborn\n", + " sns.scatterplot(x=data[:, 0], y=data[:, 1], ax=axes[0], color=\"green\", alpha=0.6, label=\"Original Data\")\n", + " sns.kdeplot(x=grid[:, 0], y=grid[:, 1], weights=prob_density, fill=True, ax=axes[0], cmap=\"viridis\", alpha=0.5)\n", + " axes[0].set_title(\"Original Data and Fitted Density\")\n", + " axes[0].legend()\n", + " axes[0].set_xlabel(\"$X_1$\")\n", + " axes[0].set_ylabel(\"$X_2$\")\n", + "\n", + " # Plot for sampled data points using Seaborn\n", + " sns.scatterplot(x=samples[:, 0], y=samples[:, 1], ax=axes[1], color=\"blue\", alpha=0.6, label=\"Sampled Data\")\n", + " sns.kdeplot(x=grid[:, 0], y=grid[:, 1], weights=prob_density, fill=True, ax=axes[1], cmap=\"plasma\", alpha=0.5)\n", + " axes[1].set_title(\"Samples and Fitted Density\")\n", + " axes[1].legend()\n", + " axes[1].set_xlabel(\"$X_1$\")\n", + " axes[1].set_ylabel(\"$X_2$\")\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + "# Sample\n", + "samples = model.sample(num_samples=n_samples)\n", + "samples.squeeze_(1)\n", + "\n", + "plot_data_and_distribution_seaborn(data, samples, model)\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-03T07:06:24.865235Z", + "start_time": "2023-11-03T07:06:20.751100Z" + } + }, + "id": "16d6c5963cd7d9db" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/simple_einet/layers/distributions/multivariate_normal.py b/simple_einet/layers/distributions/multivariate_normal.py index a7e7bd3..e6a4526 100644 --- a/simple_einet/layers/distributions/multivariate_normal.py +++ b/simple_einet/layers/distributions/multivariate_normal.py @@ -191,138 +191,3 @@ def mpe(self, num_samples) -> torch.Tensor: return samples -if __name__ == "__main__": - # The following code is a test snippet that generates multiple 2D gaussian distributions, fits a multivariate normal distribution and visualizes the data against the fitted distribution. - - # Import necessary modules - # Torch for the model and optimization, numpy for data manipulation, matplotlib for plotting - import torch - import torch.nn as nn - import torch.distributions as dist - import torch.optim as optim - import numpy as np - from typing import List - import matplotlib.pyplot as plt - - torch.manual_seed(1) - np.random.seed(1) - - import seaborn as sns - - # Apply seaborn's default style to make plots more aesthetically pleasing - sns.set_style("whitegrid") - - # Function to generate synthetic 2D data from two multivariate Gaussian distributions - # This serves as the dataset for which we want to fit a multivariate normal distribution - def generate_data(num_samples=100): - # Parameters for first Gaussian blob - mean1 = [2.0, 3.0] - cov1 = [[1.0, 0.9], [0.9, 0.5]] - - # Parameters for second Gaussian blob - mean2 = [-1.0, -2.0] - cov2 = [[0.4, -0.1], [-0.1, 0.3]] - - # Parameters for third Gaussian blob - mean3 = [4.0, -1.0] - cov3 = [[0.3, 0.2], [0.2, 0.5]] - - # Parameters for fourth Gaussian blob - mean4 = [-3.0, 2.0] - cov4 = [[0.5, -0.2], [-0.2, 0.3]] - - # Generate data points - data1 = np.random.multivariate_normal(mean1, cov1, num_samples // 4) - data2 = np.random.multivariate_normal(mean2, cov2, num_samples // 4) - data3 = np.random.multivariate_normal(mean3, cov3, num_samples // 4) - data4 = np.random.multivariate_normal(mean4, cov4, num_samples // 4) - data = np.vstack([data1, data2, data3, data4]) - - return torch.tensor(data, dtype=torch.float32) - - # Function to plot both the generated data and the learned probability density - def plot_data_and_distribution_seaborn(data, samples, model): - sns.set(style="whitegrid") - fig, axes = plt.subplots(1, 2, figsize=(12, 6), sharex=True, sharey=True) - - # Generate a grid over which we evaluate the model's density function - x, y = np.linspace(data[:, 0].min(), data[:, 0].max(), 100), np.linspace( - data[:, 1].min(), data[:, 1].max(), 100 - ) - X, Y = np.meshgrid(x, y) - grid = np.vstack([X.ravel(), Y.ravel()]).T - - # Evaluate the learned density function over the grid - with torch.no_grad(): - grid_tensor = torch.tensor(grid, dtype=torch.float32) - log_prob = model(grid_tensor) - prob_density = log_prob.exp().numpy().ravel() # Ensure this is 1-dimensional - - # Plot for original data points using Seaborn - sns.scatterplot(x=data[:, 0], y=data[:, 1], ax=axes[0], color="green", alpha=0.6, label="Original Data") - sns.kdeplot(x=grid[:, 0], y=grid[:, 1], weights=prob_density, fill=True, ax=axes[0], cmap="viridis", alpha=0.5) - axes[0].set_title("Original Data and Fitted Density") - axes[0].legend() - - # Plot for sampled data points using Seaborn - sns.scatterplot(x=samples[:, 0], y=samples[:, 1], ax=axes[1], color="blue", alpha=0.6, label="Sampled Data") - sns.kdeplot(x=grid[:, 0], y=grid[:, 1], weights=prob_density, fill=True, ax=axes[1], cmap="plasma", alpha=0.5) - axes[1].set_title("Samples and Fitted Density") - axes[1].legend() - - plt.tight_layout() - plt.show(dpi=120) - - # Generate synthetic 2D data - from sklearn.datasets import make_moons - - n_samples = 400 - data = generate_data(n_samples) - # data = torch.tensor(make_moons(n_samples=n_samples, noise=0.1, random_state=0)[0]) - - # Initialize the Multivariate Normal model - # The model will be trained to fit the synthetic data - num_features = 2 - num_channels = 1 - num_leaves = 4 - num_repetitions = 1 - cardinality = 2 - - from simple_einet.einet import Einet, EinetConfig - - cfg = EinetConfig( - num_features=num_features, - num_channels=num_channels, - num_leaves=num_leaves, - depth=0, - num_repetitions=num_repetitions, - num_classes=1, - leaf_type=MultivariateNormal, - leaf_kwargs={"cardinality": cardinality}, - ) - model = Einet(cfg) - - # Setup optimization - optimizer = optim.Adam(model.parameters(), lr=0.01) - epochs = 1000 - - # Training loop to fit the Multivariate Normal model - for epoch in range(epochs): - optimizer.zero_grad() - log_prob = model(data) - - # Negative log-likelihood as loss function - loss = -torch.mean(log_prob) - loss.backward() - optimizer.step() - - # Logging to monitor progress - if epoch % 50 == 0: - print(f"Epoch [{epoch+1}/{epochs}], Loss: {loss.item()}") - - # Sample - samples = model.sample(num_samples=n_samples) - samples.squeeze_(1) - ic(samples.shape) - - plot_data_and_distribution_seaborn(data, samples, model)